This project studies several types of instrumentation to understand basic aspects of their operation as required for optimizing their operation. Considerable time was expended on determining the cause of sometimes surprising effects of starting zone length in applications of capillary zone electrophoresis to protein separation. This set of projects was done in collaboration with chemists in the laboratory of Dr. A. Chrambach of the National Institute of Child Health and Human Development (NICHD). A second project was devoted to the optimization of measurement times to estimate values of the spin-spin relaxation time T2.. This parameter is used in MRI measurements. Dr. Sinisa Pajevic has been collaborating with Drs. P. Basser (NICHD) and C. Pierpaoli (NINDS) on the problems of representation and statistical analysis of the diffusion tensor imaging data. By measuring diffusion of water in the brain the orientation of neural fibers can be determined and appropriate color representation of such data, as reported by several radiologists, yields visualization of the neural fibers with unprecedented clarity. A paper on this subject has been submitted to the Magnetic Resonance in Medicine Journal. Diffusion tensor data belong to the class of the directional/axial data whose statistical analysis requires a special treatment. Research on statistical analysis of the diffusion tensor data is under way. Work on the image compression that Drs. Weiss and Pajevic have been doing in collaboration with Dr. A. Ling (CC) is now in its final phase in which evaluation tests will be performed by 8 radiologists on the fully digital chest X-ray images. Previously, similar tests on the smaller CT images (CAT scans) has been completed, which show that wavelet based image compression preserves high quality of the images at 20-fold compression levels. The acceptable compression levels are expected to be greater for chest X-ray images. Dr. Pajevic also works with Peter Munson on the development of the cDNA Microarray technology (CGAP project) for analysis of gene expression patterns. Due to the proximity of the imaged peaks in the microarray (comparable to the FWHM of the point spread function) there is a spill-over of activity between the neighboring peaks. To correct for this we employed a fast deconvolution algorithm. Computer simulations indicate a great improvement in quantitation of peak heights (20 % - 100 %). Improvements are also confirmed with real data